{"id":"W3118349307","doi":"10.3390/f12010076","title":"Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China","year":2021,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Université de Montréal","funders":"National Key Research and Development Program of China","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Environmental science; Remote sensing; Advanced very-high-resolution radiometer; Moderate-resolution imaging spectroradiometer; Enhanced vegetation index; Spectroradiometer; Physical geography; Climate change; Geography; Vegetation Index; Satellite; Ecology; Reflectivity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009420228,0.00005244333,0.00005106671,0.00001245856,0.00002816031,0.00002063533,0.00005753296,0.00004124648,0.0000137683],"category_scores_gemma":[0.00003464325,0.00003321188,0.000008149755,0.0001861847,0.00004370321,0.00005554435,0.00004030636,0.00008920181,0.00001556502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003512694,"about_ca_system_score_gemma":0.000003249704,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001306281,"about_ca_topic_score_gemma":0.2827977,"domain_scores_codex":[0.9995151,0.00006264411,0.0000634621,0.0001357134,0.0001229965,0.0001000694],"domain_scores_gemma":[0.9998468,0.00001610917,0.00002368735,0.00009626309,0.00000204973,0.00001513143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003225786,0.00002208676,0.9871007,0.000004516418,5.813628e-7,0.0001064202,0.002664318,0.0002483257,0.0005482119,0.00003499662,0.0009882591,0.008278318],"study_design_scores_gemma":[0.0001275715,0.00001500019,0.996052,0.00002049564,0.00000106068,0.00004552937,0.00007512434,0.001067814,0.0002816986,0.001167547,0.001098014,0.00004811657],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944094,0.00005323167,0.00003115799,0.003070139,0.00005505008,0.00008800748,1.820059e-7,0.000006056438,0.002286775],"genre_scores_gemma":[0.9994378,0.00001644405,0.0001927809,0.0001696858,0.00004052159,0.000001176865,0.000007889825,0.000003033741,0.0001306344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2814915,"threshold_uncertainty_score":0.7302893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007781610941033945,"score_gpt":0.2080833691978968,"score_spread":0.2003017582568628,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}